The increasing amount of information that is stored in electronic form has caused Information Extraction to emerge as a crucial tool when attempting to detect, extract and truly understand specific categories of information in a natural language document. Information Extraction is used in question-answering (QA) systems to load information in the QA system's corpus. Templates have been used in Natural Language Processing (NLP) for quite some time. They are currently being used in QA systems to generate more question answer pairs. Templates can also be used in topic modeling where the level of granularity in the templates created can be modified to suit the domain that is being examined.
One approach used to address Information Extraction is the creation of a series of shallow text analysis rules which are typically based on pre-defined linguistic patterns. This involves the creation of syntactic rules between words and exploits the semantic classes of words to capture concepts and events which may be of interest to the user. However, the acquisition of such domain specific knowledge and the development of such specific rules constitute an extremely time consuming task. Moreover, these tasks are restricted to specific applications and require vast amounts of manual intervention.